Optimization of thermochemical heat storage systems by controlling operating parameters and using two reactors

Direct CO2 emissions from space heating and hot water production in buildings has been on a rising trend in recent decades. It is increasingly urgent to develop efficient and low-carbon heating technologies that can reduce energy consumption and shift the load to off-peak times. This work concerns thermochemical heat storage (TCHS), which has the potential to offer flexibility to bridge the energy supply and demand mismatches, and help with load shifting. One of the technical barriers for the use of TCHS is the variation of the outlet conditions for discharge process, which limits the implementation and competitiveness of the technology. Here we propose a new method to overcome the barrier. By using packed-bed based thermochemical reactors packed with silica gel, as an example, we use a Computational Fluid Dynamic (CFD) tool to understand the effectiveness of controlling and optimising the outlet conditions of the TCHS reactor. We demonstrated that, by optimizing the packed bed, a stable outlet temperature could be achieved. Furthermore, the whole TCHS performance could be enhanced, doubling the discharging power and prolonged discharge time by 4 times while keeping the same outlet temperature

Polycomb group proteins EZH2 and EED directly regulate androgen receptor in advanced prostate cancer

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149265/1/ijc32118.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149265/2/ijc32118_am.pd

DeepDyve: Dynamic Verification for Deep Neural Networks

Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, however, suffer from various kinds of threats, such as adversarial example attacks and fault injection attacks. While there are many defense methods proposed against maliciously crafted inputs, solutions against faults presented in the DNN system itself (e.g., parameters and calculations) are far less explored. In this paper, we develop a novel lightweight fault-tolerant solution for DNN-based systems, namely DeepDyve, which employs pre-trained neural networks that are far simpler and smaller than the original DNN for dynamic verification. The key to enabling such lightweight checking is that the smaller neural network only needs to produce approximate results for the initial task without sacrificing fault coverage much. We develop efficient and effective architecture and task exploration techniques to achieve optimized risk/overhead trade-off in DeepDyve. Experimental results show that DeepDyve can reduce 90% of the risks at around 10% overhead

A sorption-based thermochemical storage and cooling system for cold chain transportation driven by engine waste heat.

The drastically increased demand for temperature-controlled environments for food/medical storage and transportation calls for more efficient and environment friendly cooling systems to reduce their effect on climate change. Commonly used cold chain transportation vehicles have an overall thermal efficiency of ~35%, implying that ~65% of energy input is rejected to the ambient as waste heat. Over the rejected waste heat, ~54% is through the exhaust gases (~400-500 degC) and ~46% though the water coolant circulation (~80-90 degC). In this work, we use the waste heat to drive a newly developed sorption based thermochemical energy storage and cooling system. Such a system uses the air and an absorbent as a working pair, and energy storage and cooling are released through air dehumidification using the desiccant material and evaporative cooling. A schematic diagram of the proposed energy storage and cooling system is shown in Fig. 1 with a brief description. The absorbent is regenerated using the rejected heat through either or both of the water coolant or the engine exhaust. The newly proposed storage and cooling system has the potential to improve the vehicle thermal efficiency and reduce CO2 and particulate emissions. It also provides an alternative to the vapor compression based refrigeration cycles with an eco-friendly system

Performance enhancement of thermochemical energy storage system through heat recovery and reutilisation

This work is concerned with sorption based thermochemical energy storage (TCES) for heating applications. Despite significant efforts in the past, the system level efficiency of such TCES remains relatively low. This work aimsto significantly enhance the efficiency. Silica gel-water pair is used as an example for the work and a packed bed is used as the reactor. With a basic open TCES system, hot air flowing through the bed is directly rejected to the ambient, leading to a low system efficiency. Through the inclusion of a heat exchange unit, we recover and reuse the heat from the outlet of the reactor to preheat inlet air during desorption (charging) to reduce the energy consumption. Our results show that the charging efficiency, defined as ratio of desorption heat to input energy, ranges only from 13% to 17% for the basic open TCES system; whereas the use of a single stage heat exchanger increases the charging efficiency to 77%~85%. The use of a two-stage heat recovery process is shown to enhance further the efficiency up to ~90%. Additionally, an optimal charging flowrate is found which corresponds to the lowest input energy. The system efficiency (ratio of discharged heat to the total input energy) is found to increase from ~10% to ~59% by using the single heat exchange unit, while can be further increased up to ~63% if the two-stage heat recovery is used

Performance enhancement of thermochemical energy storage system through heat recovery and reutilisation

This work is concerned with sorption based thermochemical energy storage (TCES) for heating applications. Despite significant efforts in the past, the system level efficiency of such TCES remains relatively low. This work aimsto significantly enhance the efficiency. Silica gel-water pair is used as an example for the work and a packed bed is used as the reactor. With a basic open TCES system, hot air flowing through the bed is directly rejected to the ambient, leading to a low system efficiency. Through the inclusion of a heat exchange unit, we recover and reuse the heat from the outlet of the reactor to preheat inlet air during desorption (charging) to reduce the energy consumption. Our results show that the charging efficiency, defined as ratio of desorption heat to input energy, ranges only from 13% to 17% for the basic open TCES system; whereas the use of a single stage heat exchanger increases the charging efficiency to 77%~85%. The use of a two-stage heat recovery process is shown to enhance further the efficiency up to ~90%. Additionally, an optimal charging flowrate is found which corresponds to the lowest input energy. The system efficiency (ratio of discharged heat to the total input energy) is found to increase from ~10% to ~59% by using the single heat exchange unit, while can be further increased up to ~63% if the two-stage heat recovery is used

Application of Luminescent Bacteria Bioassay in the Detection of Pollutants in Soil

The luminescent bacteria bioassay has been commonly used in the detection of environmental pollutants. Compared with traditional chemical and other biological detection methods, the luminescent bacteria bioassay has many demonstrated advantages such as a sensitive response, low cost, high efficiency, and environmental friendliness. The traditional luminescent bacteria bioassay has poor reproducibility and cannot achieve undisturbed soil testing, and the use of leach liquor also affects the results. This paper reviews the research progress and existing issues for the traditional luminescent bacteria bioassay used in the detection of soil pollutants. The luminescence mechanisms and detection principles of three commonly used luminescent bacteria, i.e., Vibrio fischeri, Photobacterium phosphoreum, and Vibrio qinghaiensis, are discussed and compared. In addition, two new luminescent bacteria bioassays are introduced to detect soil pollutants. One method is based on recombinant luminescent bacteria obtained with a gene-modification technique. This method can realize specific detection and enhance sensitivity, but it still cannot achieve undisturbed soil detection. The other method involves using magnetic nanoparticle (MNP)-based biosensors made from luminescent bacteria and MNPs. It can realize the accurate detection of the biological toxicity of the combined pollutants in soil without disturbing the soil’s integrity. This study shows that MNP-based biosensors have good application prospects in soil pollution detection, but the mechanism behind their utility still needs to be investigated to realize their popularization and application